Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory459.4 KiB
Average record size in memory826.7 B

Variable types

Numeric12

Alerts

"Area mean", "Area se", "Area worst", is highly overall correlated with "Compactness mean", "Compactness se", "Compactness worst", and 4 other fieldsHigh correlation
"Compactness mean", "Compactness se", "Compactness worst", is highly overall correlated with "Area mean", "Area se", "Area worst", and 8 other fieldsHigh correlation
"Concave points mean", "Concave points se", "Concave points worst", is highly overall correlated with "Area mean", "Area se", "Area worst", and 6 other fieldsHigh correlation
"Concavity mean", "Concavity se", "Concavity worst", is highly overall correlated with "Area mean", "Area se", "Area worst", and 6 other fieldsHigh correlation
"Fractal dimension mean", "Fractal dimension se", "Fractal dimension worst"] is highly overall correlated with "Compactness mean", "Compactness se", "Compactness worst", and 4 other fieldsHigh correlation
"ID number", "Diagnosis", is highly overall correlated with "Compactness mean", "Compactness se", "Compactness worst", and 1 other fieldsHigh correlation
"Perimeter mean", "Perimeter se", "Perimeter worst", is highly overall correlated with "Area mean", "Area se", "Area worst", and 4 other fieldsHigh correlation
"Radius mean", "Radius se", "Radius worst", is highly overall correlated with "Area mean", "Area se", "Area worst", and 4 other fieldsHigh correlation
"Smoothness mean", "Smoothness se", "Smoothness worst", is highly overall correlated with "Compactness mean", "Compactness se", "Compactness worst", and 4 other fieldsHigh correlation
"Symmetry mean", "Symmetry se", "Symmetry worst", is highly overall correlated with "Compactness mean", "Compactness se", "Compactness worst", and 1 other fieldsHigh correlation
"Concavity mean", "Concavity se", "Concavity worst", has 13 (2.3%) zeros Zeros
"Concave points mean", "Concave points se", "Concave points worst", has 13 (2.3%) zeros Zeros

Reproduction

Analysis started2025-01-02 19:40:01.880264
Analysis finished2025-01-02 19:40:10.537097
Duration8.66 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

df.columns = [
Real number (ℝ)

Distinct498
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020542299
Minimum0.007882
Maximum0.07895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:10.578451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.007882
5-th percentile0.011758
Q10.01516
median0.01873
Q30.02348
95-th percentile0.034988
Maximum0.07895
Range0.071068
Interquartile range (IQR)0.00832

Descriptive statistics

Standard deviation0.0082663715
Coefficient of variation (CV)0.40240733
Kurtosis7.8961298
Mean0.020542299
Median Absolute Deviation (MAD)0.00393
Skewness2.1951329
Sum11.688568
Variance6.8332898 × 10-5
MonotonicityNot monotonic
2025-01-02T20:40:10.651611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01344 4
 
0.7%
0.01454 3
 
0.5%
0.01884 3
 
0.5%
0.01897 3
 
0.5%
0.01536 3
 
0.5%
0.01647 3
 
0.5%
0.01924 3
 
0.5%
0.0187 3
 
0.5%
0.02045 3
 
0.5%
0.01594 2
 
0.4%
Other values (488) 539
94.7%
ValueCountFrequency (%)
0.007882 1
0.2%
0.009539 1
0.2%
0.009947 1
0.2%
0.01013 1
0.2%
0.01029 1
0.2%
0.01054 1
0.2%
0.01055 1
0.2%
0.01057 1
0.2%
0.01062 1
0.2%
0.01065 2
0.4%
ValueCountFrequency (%)
0.07895 1
0.2%
0.06146 1
0.2%
0.05963 1
0.2%
0.05628 1
0.2%
0.05543 1
0.2%
0.05333 1
0.2%
0.05168 1
0.2%
0.05113 1
0.2%
0.05014 1
0.2%
0.04783 1
0.2%

"ID number", "Diagnosis",
Real number (ℝ)

High correlation 

Distinct545
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0037949039
Minimum0.0008948
Maximum0.02984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:10.722076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0008948
5-th percentile0.0015216
Q10.002248
median0.003187
Q30.004558
95-th percentile0.0079598
Maximum0.02984
Range0.0289452
Interquartile range (IQR)0.00231

Descriptive statistics

Standard deviation0.002646071
Coefficient of variation (CV)0.69726956
Kurtosis26.280847
Mean0.0037949039
Median Absolute Deviation (MAD)0.001074
Skewness3.9239686
Sum2.1593003
Variance7.0016916 × 10-6
MonotonicityNot monotonic
2025-01-02T20:40:10.793410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002551 2
 
0.4%
0.003317 2
 
0.4%
0.003563 2
 
0.4%
0.002665 2
 
0.4%
0.002701 2
 
0.4%
0.005667 2
 
0.4%
0.00456 2
 
0.4%
0.001892 2
 
0.4%
0.003224 2
 
0.4%
0.002783 2
 
0.4%
Other values (535) 549
96.5%
ValueCountFrequency (%)
0.0008948 1
0.2%
0.0009502 1
0.2%
0.0009683 1
0.2%
0.001002 1
0.2%
0.001058 1
0.2%
0.001087 1
0.2%
0.001126 1
0.2%
0.00118 1
0.2%
0.001217 1
0.2%
0.001219 1
0.2%
ValueCountFrequency (%)
0.02984 1
0.2%
0.02286 1
0.2%
0.02193 1
0.2%
0.01792 1
0.2%
0.01298 1
0.2%
0.01284 1
0.2%
0.01256 1
0.2%
0.01233 1
0.2%
0.0122 1
0.2%
0.01178 1
0.2%

"Radius mean", "Radius se", "Radius worst",
Real number (ℝ)

High correlation 

Distinct457
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.26919
Minimum7.93
Maximum36.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:10.865683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7.93
5-th percentile10.534
Q113.01
median14.97
Q318.79
95-th percentile25.64
Maximum36.04
Range28.11
Interquartile range (IQR)5.78

Descriptive statistics

Standard deviation4.8332416
Coefficient of variation (CV)0.29707943
Kurtosis0.94408958
Mean16.26919
Median Absolute Deviation (MAD)2.46
Skewness1.1031152
Sum9257.169
Variance23.360224
MonotonicityNot monotonic
2025-01-02T20:40:10.930558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.36 5
 
0.9%
13.5 4
 
0.7%
13.34 4
 
0.7%
13.45 3
 
0.5%
15.05 3
 
0.5%
15.11 3
 
0.5%
16.76 3
 
0.5%
14.8 3
 
0.5%
12.4 3
 
0.5%
16.46 3
 
0.5%
Other values (447) 535
94.0%
ValueCountFrequency (%)
7.93 1
0.2%
8.678 1
0.2%
8.952 1
0.2%
8.964 1
0.2%
9.077 1
0.2%
9.092 1
0.2%
9.262 1
0.2%
9.414 1
0.2%
9.456 1
0.2%
9.473 1
0.2%
ValueCountFrequency (%)
36.04 1
0.2%
33.13 1
0.2%
33.12 1
0.2%
32.49 1
0.2%
31.01 1
0.2%
30.79 1
0.2%
30.75 1
0.2%
30.67 1
0.2%
30 1
0.2%
29.92 1
0.2%
Distinct511
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.677223
Minimum12.02
Maximum49.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:10.993431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum12.02
5-th percentile16.574
Q121.08
median25.41
Q329.72
95-th percentile36.3
Maximum49.54
Range37.52
Interquartile range (IQR)8.64

Descriptive statistics

Standard deviation6.1462576
Coefficient of variation (CV)0.23936613
Kurtosis0.22430187
Mean25.677223
Median Absolute Deviation (MAD)4.33
Skewness0.49832131
Sum14610.34
Variance37.776483
MonotonicityNot monotonic
2025-01-02T20:40:11.062449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.7 3
 
0.5%
27.26 3
 
0.5%
30.36 2
 
0.4%
23.17 2
 
0.4%
32.82 2
 
0.4%
15.77 2
 
0.4%
22.02 2
 
0.4%
33.17 2
 
0.4%
20.21 2
 
0.4%
25.48 2
 
0.4%
Other values (501) 547
96.1%
ValueCountFrequency (%)
12.02 1
0.2%
12.49 1
0.2%
12.87 1
0.2%
14.1 1
0.2%
14.2 1
0.2%
14.82 1
0.2%
15.38 1
0.2%
15.4 1
0.2%
15.54 1
0.2%
15.64 1
0.2%
ValueCountFrequency (%)
49.54 1
0.2%
47.16 1
0.2%
45.41 1
0.2%
44.87 1
0.2%
42.79 1
0.2%
41.85 1
0.2%
41.78 1
0.2%
41.61 1
0.2%
40.68 1
0.2%
40.54 1
0.2%

"Perimeter mean", "Perimeter se", "Perimeter worst",
Real number (ℝ)

High correlation 

Distinct514
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.26121
Minimum50.41
Maximum251.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.137842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum50.41
5-th percentile67.856
Q184.11
median97.66
Q3125.4
95-th percentile171.64
Maximum251.2
Range200.79
Interquartile range (IQR)41.29

Descriptive statistics

Standard deviation33.602542
Coefficient of variation (CV)0.31327767
Kurtosis1.0701497
Mean107.26121
Median Absolute Deviation (MAD)16.87
Skewness1.1281639
Sum61031.63
Variance1129.1308
MonotonicityNot monotonic
2025-01-02T20:40:11.210322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.7 3
 
0.5%
105.9 3
 
0.5%
101.7 3
 
0.5%
158.8 2
 
0.4%
119.4 2
 
0.4%
106 2
 
0.4%
95.29 2
 
0.4%
101.2 2
 
0.4%
100.9 2
 
0.4%
85.07 2
 
0.4%
Other values (504) 546
96.0%
ValueCountFrequency (%)
50.41 1
0.2%
54.49 1
0.2%
56.65 1
0.2%
57.17 1
0.2%
57.26 1
0.2%
58.08 1
0.2%
58.36 1
0.2%
59.16 1
0.2%
59.9 1
0.2%
60.9 1
0.2%
ValueCountFrequency (%)
251.2 1
0.2%
229.3 1
0.2%
220.8 1
0.2%
214 1
0.2%
211.7 1
0.2%
211.5 1
0.2%
206.8 1
0.2%
206 1
0.2%
205.7 1
0.2%
202.4 1
0.2%

"Area mean", "Area se", "Area worst",
Real number (ℝ)

High correlation 

Distinct544
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880.58313
Minimum185.2
Maximum4254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.282329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum185.2
5-th percentile331.06
Q1515.3
median686.5
Q31084
95-th percentile2009.6
Maximum4254
Range4068.8
Interquartile range (IQR)568.7

Descriptive statistics

Standard deviation569.35699
Coefficient of variation (CV)0.64656814
Kurtosis4.3963948
Mean880.58313
Median Absolute Deviation (MAD)215.6
Skewness1.8593733
Sum501051.8
Variance324167.39
MonotonicityNot monotonic
2025-01-02T20:40:11.352261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
826.4 2
 
0.4%
830.5 2
 
0.4%
1269 2
 
0.4%
725.9 2
 
0.4%
1437 2
 
0.4%
624.1 2
 
0.4%
1261 2
 
0.4%
1750 2
 
0.4%
706 2
 
0.4%
402.8 2
 
0.4%
Other values (534) 549
96.5%
ValueCountFrequency (%)
185.2 1
0.2%
223.6 1
0.2%
240.1 1
0.2%
242.2 1
0.2%
248 1
0.2%
249.8 1
0.2%
259.2 1
0.2%
268.6 1
0.2%
270 1
0.2%
273.9 1
0.2%
ValueCountFrequency (%)
4254 1
0.2%
3432 1
0.2%
3234 1
0.2%
3216 1
0.2%
3143 1
0.2%
2944 1
0.2%
2906 1
0.2%
2782 1
0.2%
2642 1
0.2%
2615 1
0.2%
Distinct411
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13236859
Minimum0.07117
Maximum0.2226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.486266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.07117
5-th percentile0.095734
Q10.1166
median0.1313
Q30.146
95-th percentile0.17184
Maximum0.2226
Range0.15143
Interquartile range (IQR)0.0294

Descriptive statistics

Standard deviation0.022832429
Coefficient of variation (CV)0.17249129
Kurtosis0.51782519
Mean0.13236859
Median Absolute Deviation (MAD)0.0147
Skewness0.415426
Sum75.31773
Variance0.00052131983
MonotonicityNot monotonic
2025-01-02T20:40:11.562887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1401 4
 
0.7%
0.1312 4
 
0.7%
0.1256 4
 
0.7%
0.1415 4
 
0.7%
0.1216 4
 
0.7%
0.1234 4
 
0.7%
0.1223 4
 
0.7%
0.1275 4
 
0.7%
0.1347 4
 
0.7%
0.1199 3
 
0.5%
Other values (401) 530
93.1%
ValueCountFrequency (%)
0.07117 1
0.2%
0.08125 1
0.2%
0.08409 1
0.2%
0.08484 1
0.2%
0.08567 1
0.2%
0.08774 1
0.2%
0.08799 1
0.2%
0.08822 1
0.2%
0.08864 1
0.2%
0.08949 1
0.2%
ValueCountFrequency (%)
0.2226 1
0.2%
0.2184 1
0.2%
0.2098 1
0.2%
0.2006 1
0.2%
0.1909 1
0.2%
0.1902 1
0.2%
0.1883 1
0.2%
0.1878 1
0.2%
0.1873 1
0.2%
0.1862 1
0.2%
Distinct529
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25426504
Minimum0.02729
Maximum1.058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.645361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.02729
5-th percentile0.071196
Q10.1472
median0.2119
Q30.3391
95-th percentile0.56412
Maximum1.058
Range1.03071
Interquartile range (IQR)0.1919

Descriptive statistics

Standard deviation0.15733649
Coefficient of variation (CV)0.6187893
Kurtosis3.0392882
Mean0.25426504
Median Absolute Deviation (MAD)0.0871
Skewness1.4735549
Sum144.67681
Variance0.024754771
MonotonicityNot monotonic
2025-01-02T20:40:11.719640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3416 3
 
0.5%
0.1486 3
 
0.5%
0.3055 2
 
0.4%
0.1963 2
 
0.4%
0.2264 2
 
0.4%
0.4061 2
 
0.4%
0.1352 2
 
0.4%
0.1788 2
 
0.4%
0.1049 2
 
0.4%
0.09866 2
 
0.4%
Other values (519) 547
96.1%
ValueCountFrequency (%)
0.02729 1
0.2%
0.03432 1
0.2%
0.04327 1
0.2%
0.04619 1
0.2%
0.04712 1
0.2%
0.04953 1
0.2%
0.05036 1
0.2%
0.05131 1
0.2%
0.05213 1
0.2%
0.05232 1
0.2%
ValueCountFrequency (%)
1.058 1
0.2%
0.9379 1
0.2%
0.9327 1
0.2%
0.8681 1
0.2%
0.8663 1
0.2%
0.7917 1
0.2%
0.7725 1
0.2%
0.7584 1
0.2%
0.7444 1
0.2%
0.7394 1
0.2%

"Concavity mean", "Concavity se", "Concavity worst",
Real number (ℝ)

High correlation  Zeros 

Distinct539
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27218848
Minimum0
Maximum1.252
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.786259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01836
Q10.1145
median0.2267
Q30.3829
95-th percentile0.68238
Maximum1.252
Range1.252
Interquartile range (IQR)0.2684

Descriptive statistics

Standard deviation0.20862428
Coefficient of variation (CV)0.7664699
Kurtosis1.6152533
Mean0.27218848
Median Absolute Deviation (MAD)0.132
Skewness1.1502368
Sum154.87525
Variance0.04352409
MonotonicityNot monotonic
2025-01-02T20:40:11.857450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
2.3%
0.4504 3
 
0.5%
0.1377 3
 
0.5%
0.1791 2
 
0.4%
0.2866 2
 
0.4%
0.2644 2
 
0.4%
0.2298 2
 
0.4%
0.1804 2
 
0.4%
0.363 2
 
0.4%
0.1423 2
 
0.4%
Other values (529) 536
94.2%
ValueCountFrequency (%)
0 13
2.3%
0.001845 1
 
0.2%
0.003581 1
 
0.2%
0.004955 1
 
0.2%
0.005518 1
 
0.2%
0.005579 1
 
0.2%
0.00692 1
 
0.2%
0.007732 1
 
0.2%
0.007977 1
 
0.2%
0.01005 1
 
0.2%
ValueCountFrequency (%)
1.252 1
0.2%
1.17 1
0.2%
1.105 1
0.2%
0.9608 1
0.2%
0.9387 1
0.2%
0.9034 1
0.2%
0.9019 1
0.2%
0.8489 1
0.2%
0.8488 1
0.2%
0.8402 1
0.2%
Distinct492
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11460622
Minimum0
Maximum0.291
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:11.923937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.024286
Q10.06493
median0.09993
Q30.1614
95-th percentile0.23692
Maximum0.291
Range0.291
Interquartile range (IQR)0.09647

Descriptive statistics

Standard deviation0.065732341
Coefficient of variation (CV)0.57354949
Kurtosis-0.53553512
Mean0.11460622
Median Absolute Deviation (MAD)0.04457
Skewness0.49261553
Sum65.210941
Variance0.0043207407
MonotonicityNot monotonic
2025-01-02T20:40:11.994468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
2.3%
0.1708 3
 
0.5%
0.04306 3
 
0.5%
0.1827 3
 
0.5%
0.06296 3
 
0.5%
0.05556 3
 
0.5%
0.07431 3
 
0.5%
0.1218 3
 
0.5%
0.1105 3
 
0.5%
0.02564 3
 
0.5%
Other values (482) 529
93.0%
ValueCountFrequency (%)
0 13
2.3%
0.008772 1
 
0.2%
0.009259 1
 
0.2%
0.01042 1
 
0.2%
0.01111 2
 
0.4%
0.01389 1
 
0.2%
0.01635 1
 
0.2%
0.01667 1
 
0.2%
0.01852 1
 
0.2%
0.02022 1
 
0.2%
ValueCountFrequency (%)
0.291 1
0.2%
0.2903 1
0.2%
0.2867 1
0.2%
0.2756 1
0.2%
0.2733 1
0.2%
0.2701 1
0.2%
0.2688 1
0.2%
0.2685 1
0.2%
0.2654 1
0.2%
0.265 1
0.2%

"Symmetry mean", "Symmetry se", "Symmetry worst",
Real number (ℝ)

High correlation 

Distinct500
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29007557
Minimum0.1565
Maximum0.6638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:12.063211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1565
5-th percentile0.2127
Q10.2504
median0.2822
Q30.3179
95-th percentile0.40616
Maximum0.6638
Range0.5073
Interquartile range (IQR)0.0675

Descriptive statistics

Standard deviation0.061867468
Coefficient of variation (CV)0.21328052
Kurtosis4.4445595
Mean0.29007557
Median Absolute Deviation (MAD)0.0342
Skewness1.4339278
Sum165.053
Variance0.0038275835
MonotonicityNot monotonic
2025-01-02T20:40:12.143764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2369 3
 
0.5%
0.3109 3
 
0.5%
0.2383 3
 
0.5%
0.2226 3
 
0.5%
0.3196 3
 
0.5%
0.2972 3
 
0.5%
0.2557 2
 
0.4%
0.2741 2
 
0.4%
0.251 2
 
0.4%
0.2434 2
 
0.4%
Other values (490) 543
95.4%
ValueCountFrequency (%)
0.1565 1
0.2%
0.1566 1
0.2%
0.1603 1
0.2%
0.1648 1
0.2%
0.1652 1
0.2%
0.1712 1
0.2%
0.1783 2
0.4%
0.1811 1
0.2%
0.1859 1
0.2%
0.189 1
0.2%
ValueCountFrequency (%)
0.6638 1
0.2%
0.5774 1
0.2%
0.5558 1
0.2%
0.544 1
0.2%
0.5166 1
0.2%
0.4882 1
0.2%
0.4863 1
0.2%
0.4824 1
0.2%
0.4761 1
0.2%
0.4753 1
0.2%
Distinct535
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083945817
Minimum0.05504
Maximum0.2075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.5 KiB
2025-01-02T20:40:12.213983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.05504
5-th percentile0.062558
Q10.07146
median0.08004
Q30.09208
95-th percentile0.11952
Maximum0.2075
Range0.15246
Interquartile range (IQR)0.02062

Descriptive statistics

Standard deviation0.018061267
Coefficient of variation (CV)0.21515387
Kurtosis5.2446106
Mean0.083945817
Median Absolute Deviation (MAD)0.00986
Skewness1.6625793
Sum47.76517
Variance0.00032620938
MonotonicityNot monotonic
2025-01-02T20:40:12.289368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07427 3
 
0.5%
0.08174 2
 
0.4%
0.0849 2
 
0.4%
0.1055 2
 
0.4%
0.08701 2
 
0.4%
0.1019 2
 
0.4%
0.08633 2
 
0.4%
0.09026 2
 
0.4%
0.08009 2
 
0.4%
0.07623 2
 
0.4%
Other values (525) 548
96.3%
ValueCountFrequency (%)
0.05504 1
0.2%
0.05521 1
0.2%
0.05525 1
0.2%
0.05695 1
0.2%
0.05737 1
0.2%
0.05843 1
0.2%
0.05865 1
0.2%
0.05871 1
0.2%
0.05905 1
0.2%
0.05932 1
0.2%
ValueCountFrequency (%)
0.2075 1
0.2%
0.173 1
0.2%
0.1486 1
0.2%
0.1446 1
0.2%
0.1431 1
0.2%
0.1409 1
0.2%
0.1405 1
0.2%
0.1403 1
0.2%
0.1402 1
0.2%
0.1364 1
0.2%

Interactions

2025-01-02T20:40:09.654560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.053558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.815403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.547609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.266944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.933565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.660295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.394589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.052195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.718574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.360685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.997032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.707638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.110131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.868532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.666938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.324202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.039893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.712955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.450201image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.103336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.771070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.415604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.101791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.762855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.218251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.927160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.724795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.381339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.094794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.766235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.511697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.153555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.822231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.470662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.152061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.814731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.278926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.986874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.774014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.433987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.146700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.819026image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.565221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.201524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.871142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.522759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.200067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.870550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.338531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.052956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.830466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.488359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.201222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.876281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.621502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.253450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.923491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.576544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.250700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.933016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.402828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.106743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.892005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.546004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.258913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.933382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.680241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.305127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.978053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.633746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.303850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.990813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.465746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.165244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.945185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.603447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.316598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.987760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.736579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.358876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.034137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.687129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.356520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:10.048246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.529341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.221139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.998806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.663594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.371457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.042787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.791595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.414165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.089858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.740919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.408400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:10.101146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.579994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.304248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.057085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.716101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.422381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.094718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.843256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.461939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.141866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.790481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.456115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:10.154099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.638725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.370653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.110947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.769676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.494256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.150008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.895330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.512804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.204461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.841957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.505368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:10.208745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.701835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.435671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.163660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.827349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.553812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.205292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.948406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.563450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.256658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.894000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.556348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:10.260099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:02.759187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:03.488300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.209741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:04.877634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:05.602660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.329655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:06.997304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:07.610801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.304424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:08.943099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-02T20:40:09.601753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-02T20:40:12.345166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
"Area mean", "Area se", "Area worst","Compactness mean", "Compactness se", "Compactness worst","Concave points mean", "Concave points se", "Concave points worst","Concavity mean", "Concavity se", "Concavity worst","Fractal dimension mean", "Fractal dimension se", "Fractal dimension worst"]"ID number", "Diagnosis","Perimeter mean", "Perimeter se", "Perimeter worst","Radius mean", "Radius se", "Radius worst","Smoothness mean", "Smoothness se", "Smoothness worst","Symmetry mean", "Symmetry se", "Symmetry worst","Texture mean", "Texture se", "Texture worst",df.columns = [
"Area mean", "Area se", "Area worst",1.0000.5500.7740.6510.1190.0070.9920.9990.2100.2480.372-0.267
"Compactness mean", "Compactness se", "Compactness worst",0.5501.0000.8440.9150.7620.5270.6130.5580.5600.5270.342-0.082
"Concave points mean", "Concave points se", "Concave points worst",0.7740.8441.0000.9020.5170.3310.8130.7810.5440.4610.365-0.141
"Concavity mean", "Concavity se", "Concavity worst",0.6510.9150.9021.0000.6230.4320.7010.6560.5190.4760.387-0.118
"Fractal dimension mean", "Fractal dimension se", "Fractal dimension worst"]0.1190.7620.5170.6231.0000.7130.1790.1270.6150.4880.1930.011
"ID number", "Diagnosis",0.0070.5270.3310.4320.7131.0000.0630.0130.3120.1730.0830.381
"Perimeter mean", "Perimeter se", "Perimeter worst",0.9920.6130.8130.7010.1790.0631.0000.9940.2410.2810.381-0.247
"Radius mean", "Radius se", "Radius worst",0.9990.5580.7810.6560.1270.0130.9941.0000.2190.2570.371-0.261
"Smoothness mean", "Smoothness se", "Smoothness worst",0.2100.5600.5440.5190.6150.3120.2410.2191.0000.5010.218-0.043
"Symmetry mean", "Symmetry se", "Symmetry worst",0.2480.5270.4610.4760.4880.1730.2810.2570.5011.0000.2270.283
"Texture mean", "Texture se", "Texture worst",0.3720.3420.3650.3870.1930.0830.3810.3710.2180.2271.000-0.105
df.columns = [-0.267-0.082-0.141-0.1180.0110.381-0.247-0.261-0.0430.283-0.1051.000

Missing values

2025-01-02T20:40:10.337190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-02T20:40:10.488483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

df.columns = ["ID number", "Diagnosis","Radius mean", "Radius se", "Radius worst","Texture mean", "Texture se", "Texture worst","Perimeter mean", "Perimeter se", "Perimeter worst","Area mean", "Area se", "Area worst","Smoothness mean", "Smoothness se", "Smoothness worst","Compactness mean", "Compactness se", "Compactness worst","Concavity mean", "Concavity se", "Concavity worst","Concave points mean", "Concave points se", "Concave points worst","Symmetry mean", "Symmetry se", "Symmetry worst","Fractal dimension mean", "Fractal dimension se", "Fractal dimension worst"]
842302M17.9910.38122.801001.00.118400.277600.300100.147100.24190.078711.09500.90538.589153.400.0063990.049040.053730.015870.030030.00619325.3817.33184.602019.00.16220.66560.71190.26540.46010.11890
842517M20.5717.77132.901326.00.084740.078640.086900.070170.18120.056670.54350.73393.39874.080.0052250.013080.018600.013400.013890.00353224.9923.41158.801956.00.12380.18660.24160.18600.27500.08902
84300903M19.6921.25130.001203.00.109600.159900.197400.127900.20690.059990.74560.78694.58594.030.0061500.040060.038320.020580.022500.00457123.5725.53152.501709.00.14440.42450.45040.24300.36130.08758
84348301M11.4220.3877.58386.10.142500.283900.241400.105200.25970.097440.49561.15603.44527.230.0091100.074580.056610.018670.059630.00920814.9126.5098.87567.70.20980.86630.68690.25750.66380.17300
84358402M20.2914.34135.101297.00.100300.132800.198000.104300.18090.058830.75720.78135.43894.440.0114900.024610.056880.018850.017560.00511522.5416.67152.201575.00.13740.20500.40000.16250.23640.07678
843786M12.4515.7082.57477.10.127800.170000.157800.080890.20870.076130.33450.89022.21727.190.0075100.033450.036720.011370.021650.00508215.4723.75103.40741.60.17910.52490.53550.17410.39850.12440
844359M18.2519.98119.601040.00.094630.109000.112700.074000.17940.057420.44670.77323.18053.910.0043140.013820.022540.010390.013690.00217922.8827.66153.201606.00.14420.25760.37840.19320.30630.08368
84458202M13.7120.8390.20577.90.118900.164500.093660.059850.21960.074510.58351.37703.85650.960.0088050.030290.024880.014480.014860.00541217.0628.14110.60897.00.16540.36820.26780.15560.31960.11510
844981M13.0021.8287.50519.80.127300.193200.185900.093530.23500.073890.30631.00202.40624.320.0057310.035020.035530.012260.021430.00374915.4930.73106.20739.30.17030.54010.53900.20600.43780.10720
84501001M12.4624.0483.97475.90.118600.239600.227300.085430.20300.082430.29761.59902.03923.940.0071490.072170.077430.014320.017890.01008015.0940.6897.65711.40.18531.05801.10500.22100.43660.20750
df.columns = ["ID number", "Diagnosis","Radius mean", "Radius se", "Radius worst","Texture mean", "Texture se", "Texture worst","Perimeter mean", "Perimeter se", "Perimeter worst","Area mean", "Area se", "Area worst","Smoothness mean", "Smoothness se", "Smoothness worst","Compactness mean", "Compactness se", "Compactness worst","Concavity mean", "Concavity se", "Concavity worst","Concave points mean", "Concave points se", "Concave points worst","Symmetry mean", "Symmetry se", "Symmetry worst","Fractal dimension mean", "Fractal dimension se", "Fractal dimension worst"]
925291B11.5123.9374.52403.50.092610.102100.111200.041050.13880.065700.23882.9041.93616.970.0082000.0298200.057380.012670.014880.00473812.48037.1682.28474.20.129800.251700.36300.096530.21120.08732
925292B14.0527.1591.38600.40.099290.112600.044620.043040.15370.061710.36451.4922.88829.840.0072560.0267800.020710.016260.020800.00530415.30033.17100.20706.70.124100.226400.13260.104800.22500.08321
925311B11.2029.3770.67386.00.074490.035580.000000.000000.10600.055020.31413.8962.04122.810.0075940.0088780.000000.000000.019890.00177311.92038.3075.19439.60.092670.054940.00000.000000.15660.05905
925622M15.2230.62103.40716.90.104800.208700.255000.094290.21280.071520.26021.2052.36222.650.0046250.0484400.073590.016080.021370.00614217.52042.79128.70915.00.141700.791701.17000.235600.40890.14090
926125M20.9225.09143.001347.00.109900.223600.317400.147400.21490.068790.96221.0268.758118.800.0063990.0431000.078450.026240.020570.00621324.29029.41179.101819.00.140700.418600.65990.254200.29290.09873
926424M21.5622.39142.001479.00.111000.115900.243900.138900.17260.056231.17601.2567.673158.700.0103000.0289100.051980.024540.011140.00423925.45026.40166.102027.00.141000.211300.41070.221600.20600.07115
926682M20.1328.25131.201261.00.097800.103400.144000.097910.17520.055330.76552.4635.20399.040.0057690.0242300.039500.016780.018980.00249823.69038.25155.001731.00.116600.192200.32150.162800.25720.06637
926954M16.6028.08108.30858.10.084550.102300.092510.053020.15900.056480.45641.0753.42548.550.0059030.0373100.047300.015570.013180.00389218.98034.12126.701124.00.113900.309400.34030.141800.22180.07820
927241M20.6029.33140.101265.00.117800.277000.351400.152000.23970.070160.72601.5955.77286.220.0065220.0615800.071170.016640.023240.00618525.74039.42184.601821.00.165000.868100.93870.265000.40870.12400
92751B7.7624.5447.92181.00.052630.043620.000000.000000.15870.058840.38571.4282.54819.150.0071890.0046600.000000.000000.026760.0027839.45630.3759.16268.60.089960.064440.00000.000000.28710.07039